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Forecasting with Generalized Bayesian Vector Autoregressions
Krannert Graduate School of Management, Purdue University, West Lafayette IN, USA.
Department of Economic Statistics, Stockholm School of Economics, Stockholm, Sweden. (Stat@oru)ORCID iD: 0000-0003-0203-4688
1993 (English)In: Journal of Forecasting, ISSN 0277-6693, E-ISSN 1099-131X, Vol. 12, no 3-4, 365-378 p.Article in journal (Refereed) Published
Abstract [en]

The effects of using different distributions to parameterize the prior beliefs in a Bayesian analysis of vector autoregressions are studied. The well-known Minnesota prior of Litterman as well as four less restrictive distributions are considered. Two of these prior distributions are new to vector autoregressive models. When the forecasting performance of the different parameterizations of the prior beliefs are compared it is found that the prior distributions that allow for dependencies between the equations of the VAR give rise to better forecasts.

Place, publisher, year, edition, pages
John Wiley & Sons, 1993. Vol. 12, no 3-4, 365-378 p.
Keyword [en]
DIFFUSE PRIOR; ENC PRIOR; NORMAL-DIFFUSE PRIOR; NORMAL-WISHART PRIOR; MINNESOTA PRIOR; MONTE-CARLO INTEGRATION; MULTIVARIATE TIME SERIES
National Category
Probability Theory and Statistics Economics Business Administration
Identifiers
URN: urn:nbn:se:oru:diva-61130DOI: 10.1002/for.3980120314ISI: A1993KZ90800013OAI: oai:DiVA.org:oru-61130DiVA: diva2:1144094
Available from: 2017-09-25 Created: 2017-09-25 Last updated: 2017-09-27Bibliographically approved

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Karlsson, Sune

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